TASC 2.4 Streamlit GUI

Show Example Input

# Example input for a typical analysis run: path = "D:\jeries\output\TASC_pickles\" ST = "rawdata.pickle" STgraph = "rawdatagraph.pickle" MorphoFeatures = ['Area', 'Ellip_Ax_B_X', 'Ellip_Ax_B_Y'] BpwFeatures = ['Velocity_Full_Width_Half_Maximum', 'Velocity_Maximum_Height'] SAMPLE = False expList = [16,17,18,19,20,21,22,23] CellSample = 2 expListT = [0,1,2,3,4,5,6,7,8,9,10,11] title = "jeries_test" k_cluster = 3 MorphoOut = False MorphoIn = False combin = [["NNIR"],["METR"],["GABY"]] singleTREAT = False singleCONTROL = True multipleCL = False nrows = 1 ncols = 12 nColor = 12 nShades = 2 nColorTreat = 0 nShadesTreat = 0 nColorLay = 3 nShadesLay = 3 figsizeEXP = (25,5) figsizeTREATS = (15,5) figsizeCL = (15,15) CON = ['NNIR'] CL = ['293T'] wellCON = ['AM001100425CHR2B02293TNNIRNOCOWH00'] controls = []
Drag and drop file hereLimit 200MB per file • XLSX
  • summary_table.xlsx
    49.9MB
AreaEllip_Ax_B_XEllip_Ax_B_YEllip_Ax_C_XEllip_Ax_C_YEllipsoidAxisLengthBEllipsoidAxisLengthCEllipticity_oblateEllipticity_prolateSphericityEccentricity
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Velocity_Full_Width_Half_MaximumVelocity_Time_of_Maximum_HeightVelocity_Maximum_HeightVelocity_Ending_ValueVelocity_Ending_TimeVelocity_Starting_ValueVelocity_Starting_Time
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Sampling
If you want to sample cells to balance well sizes, check this box.

  • expList: Indices of wells to sample (comma-separated).
  • CellSample: Sampling divisor (e.g., 2 for half, 3 for a third).

Experiments to analyze (without sampling)
Indices for the wells you wish to analyze without sampling.
You can use a comma-separated list (e.g., 0,1,2,3,4,5,6,7,8,9,10,11) or a Python range (e.g., range(0,12)).

Experiment title and number of clusters
Choose a unique title for this run. All output files will use this as a prefix.
Set the number of clusters for k-means analysis.

Feature selection
Check to analyze only kinetics (BPW) or only morphology.
If you wish to run on the entire feature list, leave both unchecked.

Treatments Combination

Specify how your treatments can be combined in your experiments.

  • Each treatment name should be 4 characters long.
  • Treatments that are not combined with each other should be placed in the same list.
  • Treatments that can be combined with others should be in separate lists.

Example:
If your treatments are HGF2, HGF7, DOX1, PHA4, PHA3:

  • If HGF2, HGF7, and DOX1 are not combined with each other, put them in the same list: ['HGF2', 'HGF7', 'DOX1']
  • If PHA4 and PHA3 are not combined with the others, put them in another list: ['PHA4', 'PHA3']
  • The format is: [['HGF2', 'HGF7', 'DOX1'], ['PHA4', 'PHA3']]

You will not find a well named 'HGF2HGF7' or 'HGF2DOX1', but you may find 'HGF7PHA4'.

Note:
If you have a control well (e.g., 'CON'), do not include it in any list.

Single/multiple treatment/control/cell lines

  • singleTREAT: True if only one treatment.
  • singleCONTROL: True if only one control.
  • multipleCL: True if multiple cell lines.

Graph Properties

These settings control the appearance and layout of your output figures. You may need to adjust them depending on your experiment and preferences.

  • nrows, ncols:
    Set the number of rows and columns in your experiment figures. For example, if you have 3 cell lines and 8 treatments, use nrows=3 and ncols=8.
    Default: nrows=0, ncols=1

  • nColor, nShades:
    Define the number of colors and shades used in the experiment figures.
    Default: nColor=0, nShades=0

  • nColorTreat, nShadesTreat:
    Number of colors and shades for treatment-specific figures. Usually, you can leave these at their default values.
    Default: nColorTreat=0, nShadesTreat=0

  • nColorLay, nShadesLay:
    Number of colors and shades for y-position (layer) figures.
    Example: nColorLay=3, nShadesLay=3

  • figsizeEXP, figsizeTREATS, figsizeCL:
    Set the figure size for experiment, treatment, and cell line figures as (width, height) tuples.
    Example:

    • figsizeEXP = (40, 15)
    • figsizeTREATS = (30, 15)
    • figsizeCL = (15, 5)

Tip: You may need to revisit and adjust these settings to get the best visualization for your data.

Chi-Squared Test Variables

  • CON:
    List of control names (comma-separated).
    Example: CON = ['CON'] or CON = ['CON1', 'CON2']

  • CL:
    List of cell lines (comma-separated).
    Example: CL = ['BT54', 'MDA2', 'MCF7'] or CL = ['293T']

  • wellCON:
    List of well names for controls (one per line, without the 'NNN0' part).
    Example:

    HA033080917CHR1C02BT54CON0WH00 HA033080917CHR1D02MDA2CON0WH00 HA033080917CHR1F02MCF7CON0WH00
  • controls:
    List of specific experiment names to use as controls (one per line).
    Example:

    BT54HGF7

    Leave blank if not used.

Experiment Names in Order (Unique)

These are the unique experiment names found in your data:

value
AM001100425CHR2B02293TNNIRNOCONNN0NNN0NNN0WH00
AM001100425CHR2B03293TNNIRNOCONNN0NNN0NNN0WH00
AM001100425CHR2B04293TNNIRNOCONNN0NNN0NNN0WH00
AM001100425CHR2C02293TMETRNNIRNOCONNN0NNN0WH00
AM001100425CHR2C03293TMETRNNIRNOCONNN0NNN0WH00
AM001100425CHR2C04293TMETRNNIRNOCONNN0NNN0WH00
AM001100425CHR2D02293TGABYNNIRNOCONNN0NNN0WH00
AM001100425CHR2D03293TGABYNNIRNOCONNN0NNN0WH00
AM001100425CHR2D04293TGABYNNIRNOCONNN0NNN0WH00
AM001100425CHR2E02293TNNIRMETRGABYNOCONNN0WH00
AM001100425CHR2E03293TNNIRMETRGABYNOCONNN0WH00
AM001100425CHR2E04293TNNIRMETRGABYNOCONNN0WH00

Number of Cells per Experiment

Index #ExperimentNumber of Cells
0AM001100425CHR2B02293TNNIRNOCONNN0NNN0NNN0WH009228
1AM001100425CHR2B03293TNNIRNOCONNN0NNN0NNN0WH0016861
2AM001100425CHR2B04293TNNIRNOCONNN0NNN0NNN0WH0023874
3AM001100425CHR2C02293TMETRNNIRNOCONNN0NNN0WH005791
4AM001100425CHR2C03293TMETRNNIRNOCONNN0NNN0WH005362
5AM001100425CHR2C04293TMETRNNIRNOCONNN0NNN0WH005362
6AM001100425CHR2D02293TGABYNNIRNOCONNN0NNN0WH005672
7AM001100425CHR2D03293TGABYNNIRNOCONNN0NNN0WH008852
8AM001100425CHR2D04293TGABYNNIRNOCONNN0NNN0WH005446
9AM001100425CHR2E02293TNNIRMETRGABYNOCONNN0WH008205
10AM001100425CHR2E03293TNNIRMETRGABYNOCONNN0WH008950
11AM001100425CHR2E04293TNNIRMETRGABYNOCONNN0WH006184

Sampling Cells

If sampling is enabled, only a subset of cells from selected wells will be used. For example, CellSample=2 means every second cell track is sampled.

Experiments to Analyze

The following experiments will be analyzed (excluding sampled experiments if sampling is enabled):

expListT:

[
0
:
0
1
:
1
2
:
2
3
:
3
4
:
4
5
:
5
6
:
6
7
:
7
8
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8
9
:
9
10
:
10
11
:
11
]

The experiment(s) you chose:

AM001100425CHR2B02293TNNIRNOCOWH00

AM001100425CHR2B03293TNNIRNOCOWH00

AM001100425CHR2B04293TNNIRNOCOWH00

AM001100425CHR2C02293TMETRNNIRNOCOWH00

AM001100425CHR2C03293TMETRNNIRNOCOWH00

AM001100425CHR2C04293TMETRNNIRNOCOWH00

AM001100425CHR2D02293TGABYNNIRNOCOWH00

AM001100425CHR2D03293TGABYNNIRNOCOWH00

AM001100425CHR2D04293TGABYNNIRNOCOWH00

AM001100425CHR2E02293TNNIRMETRGABYNOCOWH00

AM001100425CHR2E03293TNNIRMETRGABYNOCOWH00

AM001100425CHR2E04293TNNIRMETRGABYNOCOWH00

The cell line(s):

293T

293T

293T

293T

293T

293T

293T

293T

293T

293T

293T

293T

The treatments are:

Number of features: 49

The Features are:

  1. Area
  1. Acceleration
  1. Acceleration_OLD
  1. Acceleration_X
  1. Acceleration_Y
  1. Coll
  1. Coll_CUBE
  1. Confinement_Ratio
  1. Directional_Change
  1. Overall_Displacement
  1. Displacement_From_Last_Id
  1. Displacement2
  1. Ellip_Ax_B_X
  1. Ellip_Ax_B_Y
  1. Ellip_Ax_C_X
  1. Ellip_Ax_C_Y
  1. EllipsoidAxisLengthB
  1. EllipsoidAxisLengthC
  1. Ellipticity_oblate
  1. Ellipticity_prolate
  1. Instantaneous_Angle
  1. Instantaneous_Speed
  1. Instantaneous_Speed_OLD
  1. Linearity_of_Forward_Progression
  1. Mean_Curvilinear_Speed
  1. Mean_Straight_Line_Speed
  1. Current_MSD_1
  1. Final_MSD_1
  1. MSD_Linearity_R2_Score
  1. MSD_Brownian_Motion_BIC_Score
  1. MSD_Brownian_D
  1. MSD_Directed_Motion_BIC_Score
  1. MSD_Directed_D
  1. MSD_Directed_v2
  1. Sphericity
  1. Total_Track_Displacement
  1. Track_Displacement_X
  1. Track_Displacement_Y
  1. Velocity_X
  1. Velocity_Y
  1. Eccentricity
  1. Min_Distance
  1. Velocity_Full_Width_Half_Maximum
  1. Velocity_Time_of_Maximum_Height
  1. Velocity_Maximum_Height
  1. Velocity_Ending_Value
  1. Velocity_Ending_Time
  1. Velocity_Starting_Value
  1. Velocity_Starting_Time

Features Used for Analysis

0
Area
Acceleration
Acceleration_OLD
Acceleration_X
Acceleration_Y
Coll
Coll_CUBE
Confinement_Ratio
Directional_Change
Overall_Displacement
Displacement_From_Last_Id
Displacement2

Histogram of Features

Figure 1\color{blue}{\Large Figure\ 1}
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KDE Histogram of Features

Figure 2\color{blue}{\Large Figure\ 2}
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Figure 3\color{blue}{\Large Figure\ 3}
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Figure 4\color{blue}{\Large Figure\ 4}

There are 1 significant components

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Figure 5\color{blue}{\Large Figure\ 5}
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Figure 6\color{blue}{\Large Figure\ 6}
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Figure 7\color{blue}{\Large Figure\ 7}
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Figure 8\color{blue}{\Large Figure\ 8}
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Figure 9\color{blue}{\Large Figure\ 9}
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Figure 10\color{blue}{\Large Figure\ 10}
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Figure 11\color{blue}{\Large Figure\ 11}
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Figure 12\color{blue}{\Large Figure\ 12}
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Figure 13\color{blue}{\Large Figure\ 13}
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Figure 14\color{blue}{\Large Figure\ 14}
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Figure 15\color{blue}{\Large Figure\ 15}
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Figure 16\color{blue}{\Large Figure\ 16}
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Figure 17\color{blue}{\Large Figure\ 17}
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Figure 18\color{blue}{\Large Figure\ 18}
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Figure 19\color{blue}{\Large Figure\ 19}
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Figure 20\color{blue}{\Large Figure\ 20}
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Distribution all features by groups\color{blue}{\Large Distribution\ all\ features\ by\ groups}
Figure 21\color{blue}{\Large Figure\ 21}
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Figure 22\color{blue}{\Large Figure\ 22}
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Figure 23\color{blue}{\Large Figure\ 23}
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Figure 24\color{blue}{\Large Figure\ 24}
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Figure 25\color{blue}{\Large Figure\ 25}
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Figure 26\color{blue}{\Large Figure\ 26}
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Treatments p-Value

012AllNPearson Chi-square ( 2.0) = p-value = Cramer's V =
NNIRGABY49.0129.0421.95100199705695.170700.2854
NNIRMETR45.7353.051.21100165152291.361800.1857
NNIRMETRGABY39.4739.8220.71100233393023.156700.2031
NNIR29.7360.1910.0810049963
All109787
Figure 27\color{blue}{\Large Figure\ 27}
0
Figure 28\color{blue}{\Large Figure\ 28}
0

Experiments p-Value AM001100425CHR2B02293TNNIRNOCOWH00

012AllNPearson Chi-square ( 2.0) = p-value = Cramer's V =
AM001100425CHR2B03293TNNIRNOCOWH0030.3460.179.49100168611052.321400.2008
AM001100425CHR2B04293TNNIRNOCOWH0026.5967.246.17100238742408.053300.2697
AM001100425CHR2C02293TMETRNNIRNOCOWH0038.6360.650.7310057911383.224300.3035
AM001100425CHR2C03293TMETRNNIRNOCOWH0049.5748.961.4710053621123.641400.2775
AM001100425CHR2C04293TMETRNNIRNOCOWH0049.5748.961.4710053621123.641400.2775
AM001100425CHR2D02293TGABYNNIRNOCOWH0053.4929.8316.681005672405.576200.165
AM001100425CHR2D03293TGABYNNIRNOCOWH0048.5928.0823.331008852404.833500.1496
AM001100425CHR2D04293TGABYNNIRNOCOWH0045.0429.7625.191005446218.147800.1219
AM001100425CHR2E02293TNNIRMETRGABYNOCOWH0037.9532.7729.271008205209.225500.1096
AM001100425CHR2E03293TNNIRMETRGABYNOCOWH0039.7345.3714.891008950125.705600.0832
AM001100425CHR2E04293TNNIRMETRGABYNOCOWH0041.1141.1217.77100618442.235600.0523
AM001100425CHR2B02293TNNIRNOCOWH0036.7441.9621.31009228
Figure 29\color{blue}{\Large Figure\ 29}
0
Figure 30\color{blue}{\Large Figure\ 30}
0

TimeLayers p-Value 0

012AllNPearson Chi-square ( 2.0) = p-value = Cramer's V =
135.251.3913.42100368771150.947500.1238
230.5256.8712.61100347312326.221900.1786
046.739.8713.4310038179
All109787
Figure NNIR\color{blue}{\Large Figure\ NNIR}
Figure 31\color{blue}{\Large Figure\ 31}
0
0
Figure NNIRGABY\color{blue}{\Large Figure\ NNIRGABY}
Figure 32\color{blue}{\Large Figure\ 32}
0
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Figure NNIRMETR\color{blue}{\Large Figure\ NNIRMETR}
Figure 33\color{blue}{\Large Figure\ 33}
0
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Figure NNIRMETRGABY\color{blue}{\Large Figure\ NNIRMETRGABY}
Figure 34\color{blue}{\Large Figure\ 34}
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Figure 35\color{blue}{\Large Figure\ 35}
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Figure 36\color{blue}{\Large Figure\ 36}
0
0

Figure 37\color{blue}{\Large Figure\ 37}

Descriptive Table\color{blue}{\Large Descriptive\ Table}

GroupsNMeanstd. Deviationstd. Error95 confidence Interval for Mean Upper Bound95 confidence Interval for Mean Lower BoundNMeanstd. Deviationstd. Error95 confidence Interval for Mean Upper Bound95 confidence Interval for Mean Lower BoundNMeanstd. Deviationstd. Error95 confidence Interval for Mean Upper Bound95 confidence Interval for Mean Lower Bound
0414071814.536600496393622.71458370439283.0602165085642921820.53455242508021808.538648567705941407-0.4713700351930658.1277568827879140.0399423691707079-0.3930839369600411-0.54965613342608894140711.3946887724377927.7354949034797750.03801466968177047411.46919662529659511.320180919578993
153925799.5565271035708244.14659843363871.0513700492977458801.617193293149797.495860913992753925-0.48201045157577417.1273409166704380.030692513510313827-0.42185368288513136-0.5421672202664168539259.45663785351456.9617537134869110.0299794442843199929.515397019481149.39787868754786
2144551165.293699710916589.91418783402924.9065902710245011174.91028398618281155.67711543564914455-0.1400536292712982515.710154123413020.1306686480304170.11604806182370689-0.39615532036630341445517.6472839090038712.3218301627027420.1024863840273380617.84815027334785517.446417544659887

Figure 38\color{blue}{\Large Figure\ 38}

ANOVA âˆ’ OneWay\color{blue}{\Large ANOVA\ -\ OneWay}

Could not convert column: Experiment

Could not convert column: Treatments

ANOVA Table feature per Group\color{blue}{\Large ANOVA\ Table\ feature\ per\ Group}
Sum of SquaresdfMean SquareFSig.Sum of SquaresdfMean SquareSum of Squaresdf
Area24199871121.4377212099935560.7188554664.91681969461024300399650.83109784221347.369842873348500270772.2677109786
Acceleration1430.98400528944942715.49200264472478.68722927148290.00016884288975272769041959.3593781310978482.361358297913449043390.343383418109786
Acceleration_OLD765843.87051473532382921.93525736775770.07405560654307285643.36179491110978466.363435125290688051487.232309647109786
Acceleration_X33.81293991219613216.9064699560980640.17309699757761980.841056260037504710722657.9526775410978497.6704980022365810722691.765617453109786
Acceleration_Y458.387236686171552229.193618343085772.23413447135243940.1070896433085741911262434.074053552109784102.5872082822046111262892.461290238109786
Coll22.496987134898646211.248493567449323158.217649622269482.4310018736154865e-697805.1002575065491097840.071095061734920837827.597244641447109786
Coll_CUBE20.463977045572477210.231988522786239162.380641140673023.829000367968849e-716917.7496781308041097840.063012366812384356938.213655176376109786
Confinement_Ratio494.89354497806362247.44677248903186983.50733129459303889.9789435604311097840.0354330225129384154384.8724885384945109786
Directional_Change39.590383220109594219.7951916100547973.25838705580920.0384540848473228666954.31773330461097846.075150456654017666993.9081165247109786
Overall_Displacement374718891.147153142187359445.5735765738268.738964878850537490127.16951041097844895.887626334533912209018.3166635109786
Displacement_From_Last_Id1197230.9344647522598615.46723237610503.3328811162306256909.23047799410978456.992906347719117454140.164942746109786
Displacement250583943132793.8225291971566396.911827.5936421547410234760669875319.51097842138386922.2775586285344613008113.3109786

ANOVA & Tukey HSD Results

ANOVA Results

sum_sqdfFPR(>F)
C(Experiment)31615.42482263218311103.741493115335898.216526564585167e-224
Residual253941.508597259449166

Tukey HSD Post-hoc Test

group1group2meandiffp-adjlowerupperreject
0B02293TNNIRNOCOB03293TNNIRNOCO-2.47570-3.2945-1.6569true
1B02293TNNIRNOCOB04293TNNIRNOCO-3.65040-4.4398-2.861true
2B02293TNNIRNOCOC02293TMETRNNIRNOCO-4.62480-5.6777-3.572true
3B02293TNNIRNOCOC03293TMETRNNIRNOCO-3.14840-4.1879-2.109true
4B02293TNNIRNOCOC04293TMETRNNIRNOCO-3.14840-4.1879-2.109true
5B02293TNNIRNOCOD02293TGABYNNIRNOCO0.02721-0.92310.9774false
6B02293TNNIRNOCOD03293TGABYNNIRNOCO1.566400.71332.4196true
7B02293TNNIRNOCOD04293TGABYNNIRNOCO0.31360.9933-0.59171.219false
8B02293TNNIRNOCOE02293TNNIRMETRGABYNOCO0.27940.9968-0.59891.1577false
9B02293TNNIRNOCOE03293TNNIRMETRGABYNOCO-1.92570-2.8153-1.0361true
10B02293TNNIRNOCOE04293TNNIRMETRGABYNOCO-0.81830.1488-1.74770.1111false
11B03293TNNIRNOCOB04293TNNIRNOCO-1.17470-1.8552-0.4942true